Artificial photosynthesis optimization

ABSTRACT

A method for photosynthesis optimization including determining ambient levels of at least one gas, water, and sunlight at a location. A catalyst is selected to perform an artificial photosynthesis reaction at the location. At least one limiting factor is determined for the artificial photosynthesis reaction based on the catalyst and the ambient levels, and the at least one limiting factor is compensated for.

BACKGROUND

Exemplary embodiments of the present inventive concept relate toartificial photosynthesis, and more particularly, to artificialphotosynthesis optimization.

Artificial photosynthesis is a chemical process that mimics naturalphotosynthesis principles with up to a 14-fold greater efficiency.Artificial photosynthesis reactions may reduce anthropogenic emissionsof carbon dioxide (CO₂) and produce useful by-products (e.g., fuel,food, chemicals (e.g., organic compounds), plastics, etc.) in theprocess. In artificial photosynthesis reactions, a catalyst (e.g., anartificial leaf, a semiconductor, etc.) must be able to use sunlight andwater (H₂O) to reduce CO₂ and H₂O into H₂.

However, the availability of CO₂, H₂O and sunlight is often not presentwith required/optimal proportions at the location of an artificialphotosynthesis reaction. For example, levels of sunlight, CO₂ andhumidity can fluctuate widely depending on numerous conditions, such asa location, atmospheric pollutants, season, weather, time of day, etc. Abig challenge with artificial photosynthesis reactions is getting acatalyst's sunlight-charged particles to persist long enough to performthe chemical reactions for utilisation. The catalyst's charged particlesseparate when the sunlight's energy is absorbed, but they can also comeback together very quickly. There is a need to control humidity andsunlight at the location of an artificial photosynthesis reaction tokeep all the charged particles engaged completely in the chemicalreaction for maximum utilization, and to capture CO₂ to the fullestextent possible. However, excessive or sustained sunlight can alsodamage a catalyst. Furthermore, optimal input levels may depend on atype of catalyst being used and a cost-benefit-analysis of adjustinginputs. To provide the greatest efficiency for artificial photosynthesisreactions, consideration of these variables must be carefully performed.

SUMMARY

Exemplary embodiments of the present inventive concept relate to amethod, a computer program product, and a system for providingphotosynthesis optimization.

According to an exemplary embodiment of the present inventive concept, amethod may be provided for photosynthesis optimization includingdetermining ambient levels of at least one gas, water, and sunlight at alocation. A catalyst is selected to perform an artificial photosynthesisreaction at the location. At least one limiting factor is determined forthe artificial photosynthesis reaction based on the catalyst and theambient levels, and the at least one limiting factor is compensated for.

According to an exemplary embodiment of the present inventive concept, acomputer program product may be provided for photosynthesisoptimization. The computer program product may include one or morenon-transitory computer-readable storage media and program instructionsstored on the one or more non-transitory computer-readable storage mediacapable of performing a method. The method includes determining ambientlevels of at least one gas, water, and sunlight at a location. Acatalyst is selected to perform an artificial photosynthesis reaction atthe location. At least one limiting factor for the artificialphotosynthesis reaction is determined based on the catalyst and theambient levels, and the at least one limiting factor is compensated for.

According to an exemplary embodiment of the present inventive concept, acomputer system for photosynthesis optimization may be provided. Thesystem includes one or more computer processors, one or morecomputer-readable storage media, and program instructions stored on theone or more of the computer-readable storage media for execution by atleast one of the one or more processors capable of performing a method.The method includes determining ambient levels of at least one gas,water, and sunlight at a location. A catalyst is selected to perform anartificial photosynthesis reaction at the location. At least onelimiting factor for the artificial photosynthesis reaction is determinedbased on the catalyst and the ambient levels, and the at least onelimiting factor is compensated for.

BRIEF DESCRIPTION OF THE DRAWINGS

The following detailed description, given by way of example and notintended to limit the exemplary embodiments solely thereto, will best beappreciated in conjunction with the accompanying drawings, in which:

FIG. 1 illustrates a schematic diagram of a photosynthesis optimizationsystem 100, in accordance with an exemplary embodiment of the presentinventive concept.

FIG. 2A illustrates a flowchart of photosynthesis optimization 200provided by a photosynthesis optimization program 134 of thephotosynthesis optimization system 100, in accordance with an exemplaryembodiment of the present inventive concept.

FIG. 2B illustrates a catalyst 3D lookup table used by thephotosynthesis optimization program 134 to implement the photosynthesisoptimization 200 of FIG. 2A, according to an exemplary embodiment of thepresent inventive concept.

FIG. 2C illustrates an exemplary methodology of prediction horizonoptimization used by the photosynthesis optimization program 134 toimplement the photosynthesis optimization 200 of FIG. 2A.

FIG. 2D illustrates an exemplary system used by the photosynthesisoptimization program 134 to implement the photosynthesis optimization200 of FIG. 2A.

FIG. 3 illustrates a block diagram depicting the hardware componentsincluded in the photosynthesis optimization system 100 of FIG. 1 , inaccordance with an exemplary embodiment of the present inventiveconcept.

FIG. 4 illustrates a cloud computing environment, in accordance with anexemplary embodiment of the present inventive concept.

FIG. 5 illustrates abstraction model layers, in accordance with anexemplary embodiment of the present inventive concept.

It is to be understood that the included drawings are not necessarilydrawn to scale/proportion. The included drawings are merely schematicexamples to assist in understanding of the present inventive concept andare not intended to portray fixed parameters. In the drawings, likenumbering may represent like elements.

DETAILED DESCRIPTION OF THE DRAWINGS

Exemplary embodiments of the present inventive concept are disclosedhereafter. However, it shall be understood that the scope of the presentinventive concept is not limited thereto. The disclosed exemplaryembodiments are merely illustrative of the claimed system, method, andcomputer program product. The present inventive concept may be embodiedin many different forms and should not be construed as limited to onlythe exemplary embodiments set forth herein. Rather, these includedexemplary embodiments are provided for completeness of disclosure and tofacilitate an understanding to those skilled in the art. In the detaileddescription, discussion of well-known features and techniques may beomitted to avoid unnecessarily obscuring the presented exemplaryembodiments.

References in the specification to “one embodiment,” “an embodiment,”“an exemplary embodiment,” etc., indicate that the embodiment describedmay include a particular feature, structure, or characteristic, but notevery embodiment may necessarily include that feature, structure, orcharacteristic. Moreover, such phrases are not necessarily referring tothe same embodiment. Further, when a particular feature, structure, orcharacteristic is described in connection with an embodiment, it issubmitted that it is within the knowledge of one skilled in the art toimplement such feature, structure, or characteristic in connection withother embodiments whether or not explicitly described.

In the interest of not obscuring the presentation of the exemplaryembodiments of the present inventive concept, in the following detaileddescription, some processing steps or operations that are known in theart may have been combined for presentation and for illustrationpurposes, and in some instances, may have not been described in detail.Additionally, some processing steps or operations that are known in theart may not be described at all. The following detailed description isfocused on the distinctive features or elements of the present inventiveconcept according to various exemplary embodiments.

As previously mentioned, efficient artificial photosynthesis reactionsdepend on, for example, optimal levels of ambient CO₂, H₂O, andsunlight. These input levels can vary according to the location,atmospheric pollutants, season, weather, time of day, catalyst type,cost-benefit-analysis, etc. A human being is incapable of computing allthese variables simultaneously with any degree of expediency oraccuracy, and even less so with calculating the logistics ofadjustments. The present inventive concept enables artificialphotosynthesis optimization based on analysis of these variables toprovide higher product yields, reduced expense, and reduced diversion ofhuman resources.

FIG. 1 depicts a schematic diagram of the photosynthesis optimizationsystem 100, in accordance with an exemplary embodiment of the presentinventive concept.

The photosynthesis optimization system 100 may include a user-operatedcomputing device 120 and a photosynthesis optimization server 130, whichmay all be interconnected via a network 108. Programming and datacontent may be stored and accessed remotely across several servers viathe network 108. Alternatively, programming and data may be storedlocally on as few as one physical computing device 120 or stored amongstmultiple computing devices.

According to the exemplary embodiment of the present inventive conceptdepicted in FIG. 1 , the network 108 may be a communication channelcapable of transferring data between connected devices. The network 108may be the Internet, representing a worldwide collection of networks 108and gateways to support communications between devices connected to theInternet. Moreover, the network 108 may utilize various types ofconnections such as wired, wireless, fiber optic, etc., which may beimplemented as an intranet network, a local area network (LAN), a widearea network (WAN), or a combination thereof. The network 108 may be aBluetooth network, a Wi-Fi network, or a combination thereof. Thenetwork 108 may operate in frequencies including 2.4 GHz and 5 GHzinternet, near-field communication, Z-Wave, Zigbee, etc. The network 108may be a telecommunications network used to facilitate telephone callsbetween two or more parties comprising a landline network, a wirelessnetwork, a closed network, a satellite network, or a combinationthereof. In general, the network 108 may represent any combination ofconnections and protocols that will support communications betweenconnected devices.

The computing device 120 may include a photosynthesis optimizationclient 122, and may be an enterprise server, a laptop computer, anotebook, a tablet computer, a netbook computer, a personal computer(PC), a desktop computer, a server, a personal digital assistant (PDA),a smart phone, a mobile phone, a virtual device, a thin client, an IoTdevice, or any other electronic device or computing system capable ofsending and receiving data to and from other computing devices. Thecomputing device 120 may be connected to various measuring devices, suchas for measuring ambient levels of gases, humidity, and/or light.Although the computing device 120 is shown as a single device, thecomputing device 120 may be comprised of a cluster or plurality ofcomputing devices, in a modular manner, etc., working together orworking independently.

The computing device 120 is described in greater detail as a hardwareimplementation with reference to FIG. 3 , as part of a cloudimplementation with reference to FIG. 4 , and/or as utilizing functionalabstraction layers for processing with reference to FIG. 5 .

The photosynthesis optimization client 122 may act as a client in aclient-server relationship with a server, for example the photosynthesisoptimization server 130. The photosynthesis optimization client 122 maybe a software and/or a hardware application capable of communicatingwith and providing a user interface for a user to interact with thephotosynthesis optimization server 130 and/or other computing devicesvia the network 108. Moreover, the photosynthesis optimization client122 may be capable of transferring data between the computing device 120and other computer devices/servers via the network 108. Thephotosynthesis optimization client 122 may utilize various wired andwireless connection protocols for data transmission and exchange,including Bluetooth, 2.4 GHz and 5 GHz internet, near-fieldcommunication, etc. The photosynthesis optimization client 122 isdescribed in greater detail with respect to FIGS. 2-5 .

The photosynthesis optimization server 130 may include a photosynthesisoptimization repository 132 for storing various data (describedhereinafter) and the photosynthesis optimization program 134 (alsodescribed hereinafter). The photosynthesis optimization server 130 mayact as a server in a client-server relationship with a client, e.g., thephotosynthesis optimization client 122. The photosynthesis optimizationserver 130 may be an enterprise server, a laptop computer, a notebook, atablet computer, a netbook computer, a personal computer (PC), a desktopcomputer, a server, a personal digital assistant (PDA), a rotary phone,a touchtone phone, a smart phone, a mobile phone, a virtual device, athin client, an IoT device, or any other electronic device or computingsystem capable of sending and receiving data to and from other computingdevices. Although the photosynthesis optimization server 130 is shown asa single computing device, the present inventive concept is not limitedthereto. For example, the photosynthesis optimization server 130 may becomprised of a cluster or plurality of computing devices, in a modularmanner, etc., working together or working independently.

The photosynthesis optimization server 130 is described in greaterdetail as a hardware implementation with reference to FIG. 3 , as partof a cloud implementation with reference to FIG. 4 , and/or as utilizingfunctional abstraction layers for processing with reference to FIG. 5 .The photosynthesis optimization program 134 and/or the photosynthesisoptimization client 122 may be software and/or hardware programs thatmay facilitate photosynthesis optimization discussed in further detailwith reference to FIGS. 2-5 .

FIG. 2A illustrates the flowchart of photosynthesis optimization 200, inaccordance with an exemplary embodiment of the present inventiveconcept.

The photosynthesis optimization program 134 may determine ambient levelsof gases, water, and light (step 202). The ambient levels of gases(e.g., CO₂, oxygen (O₂), pollutants, etc.), water (e.g., humidity), andlight (e.g., sunlight) may be dynamically measured and/or predicted at apredetermined location. Ambient levels may refer to quantifiedmeasurements of gas (e.g., partial pressures), water (e.g., humidity),and light (e.g., irradiance intensity). The location may host or be aprospective host for a catalyst (e.g., an artificial photosynthesissubstrate and/or a photosynthetic organism).

The location may be an area with predetermined boundaries (e.g., ageolocation, region, town/city, property, activity site, an enclosedspace, etc.). A geolocation may refer to a location based on a specificlongitude and latitude. A property may refer to a location based ondimensions of real property. An activity site may refer to a location ofanthropogenic gaseous emission (e.g., CO₂) production, such as anindustrial complex or part thereof (e.g., a smokestack). An enclosedspace may refer to a location with tangible boundaries (e.g., a walledstructure, an artificial photosynthesis unit, a greenhouse, etc.). Thelocation may be selected by the user or the photosynthesis optimizationprogram 134. The photosynthesis optimization program 134 may select thelocation based on a catalyst and/or measured or predicted ambient levelsof gases, water, and light. Similarly, the photosynthesis optimizationprogram 134 may select a catalyst based on a location and/or measured orpredicted ambient levels of gases, water, and light.

The photosynthesis optimization program 134 may obtain ambient levels ofthe gases, water, and light at the location via the network 108 from alocal computing device 120 and/or the internet. Multimedia may beobtained related to the location (e.g., satellite imaging (e.g.,irradiance and column average CO₂), bottom-up emission inventories,terrain topography, location dimensions, weather forecasts (e.g.,temperature, relative humidity, precipitation, pressure profile),industrial project details, almanacs, and/or published ambient levels ofgases, water, and light, etc. The photosynthesis optimization program134 may perform machine learning (e.g., natural language processing(NLP), optical character recognition (OCR), etc.) and extract relevantlocation features and/or ambient levels of gases, water, and light. Aspatial temporal learning model may be generated to predict solarirradiance, column CO₂ averages, and humidity at the location based oncorrelation with corroborating measurements and/or extracted features.

For example, the photosynthesis optimization program 134 may measure theambient levels of CO₂, humidity, and sunlight at a factory premisesusing a computing device 120 thereat. The ambient level measurementstaken may be correlated with weather forecasts and/or satellite imaging.A spatial temporal model may learn ambient levels of CO₂, humidity, andsunlight at the same location, and predict ambient levels at otherlocations with similar characteristics. Satellite imaging analysis of anearby stretch of highway reveals comparable ambient levels of CO₂,humidity, and sunlight.

The photosynthesis optimization program 134 may calculate ideal levelsof ambient gases, water, and light for a reaction (step 204). The ideallevels of gasses, water, and light may refer to peakproportions/concentrations calculated for a photosynthetic reaction(natural and/or artificial) as performed by at least one catalyst at thelocation. In an embodiment, with reference to FIG. 2B, a methodology ofprediction horizon optimization may be used. Predicted and/or dynamicideal levels of gases, water, and light may vary by time (e.g., hour,day, season, etc.). For example, there may be increased ambient CO₂emissions during rush hour and comparatively less on weekends, etc.Thus, ideal levels of gases, water, and light might not be static at thelocation. Different segments of time of predetermined lengths may havedifferent predicted and/or dynamic ideal levels of gases, water, andlight. The predetermined length of time may itself be variable (e.g.,daytime, or for the duration of a condition). The type of catalyst mayinclude a plant, photocatalyst, photochemical catalyst,bio-electrochemical catalyst, etc. An enclosed catalyst (e.g., acontrolled environment) may refer to an artificial photosynthesis unit.In a cooperative network of catalysts and/or artificial photosynthesisunits, catalyst types may be mixed. The catalyst may be selected basedon the ambient levels of gases, water, and light at the location,reaction products produced (e.g., CO, O₂, H₂, CxHy, etc.), and/or thelocation. The photosynthesis optimization program 134 may select acatalyst (e.g., for the location) based on a sensitivity profilepublished by the manufacturer which includes the limitations and/orideal ambient levels of gases, water, and light. For example, thesensitivity profile for the catalyst may include the impact of ambientlevels of gases, humidity, and irradiance on CO₂ sequestration,longevity, and/or reaction performance. Each type of catalyst may have aunique sensitivity profile. The sensitivity profile may be analyzed bymachine learning processes (e.g., NLP and/or OCR). In an embodiment, thesensitivity profile may also include temperature tolerance. Thephotosynthesis optimization program 134 may generate a three-dimensional(3D) lookup table for each catalyst that graphs the catalyst'ssensitivity profile. In an embodiment, catalyst dimensions, quantity,and/or estimated wear-and-tear may be accounted for in the 3D lookup.The catalyst type; reaction products; ideal ambient levels of gases,water, and light; sensitivity profile; catalyst 3D lookup table;manufacturer multimedia; and measured ambient levels of gases, water,and light at the location may be stored in the photosynthesisoptimization repository 132.

For example, with reference to FIG. 2B, the photosynthesis optimizationprogram 134 may select a catalyst for inclusion in a network ofartificial photosynthesis units based on the referenced 3D lookup tablewhich permits an ideal artificial photosynthesis reaction given theambient levels of CO₂, humidity, and sunlight at the factory premises.

The photosynthesis optimization program 134 may calculate potentialadjustments to ambient levels of gases, water, and light for the idealreaction (step 206). The ideal reaction may be an ideal photosyntheticreaction (artificial and/or natural) for at least one selected catalystand/or location. If multiple different catalysts are involved withunique ideal reactions, the artificial photosynthesis program 134determine an optimized compromise. The photosynthesis optimizationprogram 134 may compare ideal ambient levels for the ideal reaction withthe ambient levels at the location and detect a limiting factor. Alimiting factor may refer to a sub-ideal catalyst, proportion/ambientlevel of gases, water, and/or light that hinders the ideal reaction. Thephotosynthesis optimization program 134 may refer to the spatialtemporal model (if already generated). The inputs to the spatialtemporal model may include the levels of ambient gases, water, andlight, location data (e.g., climate, day, time, season, etc.), theselected catalyst, etc. Based on the spatial temporal model, the idealreaction and potential adjustments may be retrieved from the artificialphotosynthesis repository 132. An illumination control system and/or anartificial light source may be used to adjust light irradiance to atleast a portion of the at least one catalyst. The photosynthesisoptimization program 134 may use IoT feed to determine the catalyst'sirradiance level and distribution thereof. The photosynthesisoptimization program 134 may virtually partition the catalyst intosections and analyse irradiance accordingly. The illumination controlsystem may include lenses and/or reflectors. The lenses and/orreflectors may be connected to inclinometers which adjust the angleand/or position of the lenses and/or reflectors in response to actuatorsoperated by a controller connected to the photosynthesis optimizationprogram 134. The photosynthesis optimization program 134 may performcalculations to increase irradiance (e.g., angles of light incidence,angles of light reflection, lens angles, etc.) to at least a part of thecatalyst (e.g., a section with poor irradiance). The controller may beconnected to a battery, processor, and an emergency switch. A humiditycontroller (e.g., a humidifier) may be used to adjust ambient levels ofwater. The photosynthesis optimization program 134 may determine theamount of water needed to produce the desired humidity (e.g., based onthe volume/pressure of the ambient levels of gases and/or the area of anenclosed location, rate of atmospheric dispersal (if open), temperature,etc.). In a network of physically connected artificial photosynthesisunits, potential adjustments may include diverting gases (e.g., CO₂),humidity, and/or sunlight from one artificial photosynthesis unit(s) toanother, such as by interconnected pipes, vents, and/or valves.

For example, the photosynthesis optimization program 134 may detectdecreased levels of CO₂ emanating from the factory in the IoT feed andbeing sequestered by one of the artificial photosynthesis units.Analysis of a press release indicates that the factory is down formaintenance and thus operations are halted. Fortunately, the otherartificial photosynthesis unit has an excess of ambient CO₂ in itsvicinity. The photosynthesis optimization program 134 determines thatCO₂ can be diverted from the other artificial photosynthesis unit toanother artificial photosynthesis unit in the network via pipes tocontinue an ideal reaction. However, the IoT feed detects decreasedirradiance too. Analysis of local weather forecasts indicates that thefactory premises will be partly cloudy for the remainder of the day. Thephotosynthesis optimization program 134 calculates that artificialsunlight will be necessary to preserve the ideal reaction and thatadjusting the illumination control system alone will be insufficient.

The photosynthesis optimization program 134 may determine if thepotential adjustments are within a cost-benefit threshold (decision208). The photosynthesis optimization program 134 may perform acost-benefit analysis before implementing the potential adjustments foran optimized reaction. The photosynthesis optimization program 134 maycalculate the ideal reaction's potential adjustment costs (e.g.,financial and opportunity costs, etc.) and benefits (e.g., quantifiedpollutant reduction or oxygen increase, net profit, product yield, valueof “green company” recognition, etc.). A threshold for cost expenditureand/or minimum benefit (cost-benefit) may be predetermined (e.g., by theuser). Financial resource costs may refer to the monetary cost involvedwith implementing potential adjustments, such as labour and thepredicted task time (e.g., dollars per minute); expenses related toadjusting gases, water, and/or light; and/or fuel and/or electricityexpenses to operate, transport, and/or rent equipment for the potentialadjustments (e.g., the humidity controller, the illumination controlsystem, etc.). The photosynthesis optimization program 134 may access anenterprise server with employee wages/job responsibilities/titles and/orequipment/material inventory databases (e.g., the enterprise, supplier,rental company, etc.) over the network 108. The photosynthesisoptimization program 134 may determine deficiencies and/or costs ofpotential adjustments. Opportunity costs may include the foregonebenefit of diverting a resource from one task to another, such asdepleting/diverting supply inventory, reallocating equipment/personsfrom another task and/or location, and the cost of reduced longevity ofequipment and/or the selected catalyst. If the cost of the potentialadjustments for the optimized reaction exceeds the predeterminedthreshold and/or falls short of a minimum benefit, the photosynthesisoptimization program 134 may present the discrepancy for the user'sapproval/rejection. A plurality of cost-benefit compliant potentialadjustment options with different cost-benefit values may be displayedto the user or may be selected automatically based on the nearest match.The cost-benefit values may refer to an aggregate score or individualcost and benefit scores. Time segments with different potentialadjustments may have different associated cost-benefit values and/orthresholds. In an embodiment, a predetermined cost-benefit buffer zonemay be used to allow for unanticipated expenses and delays to theoptimized reaction. If the user overrides the optimized reaction, thephotosynthesis optimization client 134 may learn accordingly. Acost-benefit analysis model component may be included in the spatialtemporal model.

If the answer to decision 208 is “YES”, the photosynthesis optimizationprogram 134 may proceed to step 208 a and perform the adjustments for anoptimized reaction. The optimized reaction may refer to a reaction thatmaximizes the cost-benefit value within the predetermined cost-benefitthreshold. Thus, the optimized reaction may or may not be the same asthe ideal reaction. The photosynthesis optimization program 134 maycoordinate logistics of implementing the optimized reaction. Forexample, the photosynthesis optimization program 134 may provideinstructions, scheduling, and/or dispatch humans, remotely operatedequipment, and/or machines (e.g., drones) to implement the adjustmentsand/or the modified adjustments (described below) for the optimizedreaction. The spatial temporal model may learn from the context andefficacy of implemented adjustments and/or modified adjustments.

If the answer to decision 208 is “NO”, the photosynthesis optimizationprogram 134 may proceed to step 208 b and calculate and perform modifiedadjustments for an optimized reaction including cost-benefit valueparameters. The modified adjustments may include altering at least oneof the location, the catalyst(s), use of resources (e.g., equipment,machines, employees, etc.), time of operation, and quantities of gases,water, and light (e.g., proportions, quantities, etc.).

For example, the photosynthesis optimization program 134 may determinethat the cost of diverting the CO₂ from the other artificialphotosynthesis unit involves a negligible cost. However, the cost ofproducing artificial sunlight for both artificial photosynthesis unitswill be substantial over the span of 9 hours required (10 am to 7 pm).Moreover, the cost-benefit analysis model indicates that artificialsunlight bulbs have an impermissibly high tendency to malfunction whenoperated for several hours continuously, which increases projectedmaintenance costs and reduces the net benefit. The photosynthesisoptimization program 134 also determines that the illumination controlsystem can be adjusted sub-ideally, with little cost, but that theresultant benefit is below a threshold. The photosynthesis optimizationprogram 134 determines that transporting the artificial photosynthesisunits to either the nearby stretch of highway or another factoryinvolves an acceptable cost of fuel and labour. Although the satelliteimaging and weather forecasts demonstrate that the ambient levels ofCO₂, humidity, and sunlight are no longer ideal at the nearby stretch ofhighway—the other factory is considerably further away which entailssignificant cost at prevailing gas prices in the area retrieved via thenetwork 108. Thus, the potential adjustments for the optimized reactionbased on the cost-benefit analysis is to move the operation to thenearby stretch of highway and install the illumination control system toreflect concentrated light with minimal need for artificial sunlight.The photosynthesis optimization program 134 communicates to auser-operated computing device 120 on-site at the factory andcoordinates the move which will require trucks and three other staffedindividuals to assist in transporting the artificial photosynthesisunits and the illumination control system. Once setup at the nearbystretch of highway, the illumination control system reflectors areautomatically adjusted to maximize irradiance to the artificialphotosynthesis units between 10 am and 5 pm, and between 5 pm and 7 pm,minimal artificial sunlight will be used.

In accordance with the exemplary embodiment of the present inventiveconcept illustrated with reference to FIG. 2C, the photosynthesisoptimization program 134 may use the shown system architecture toperform photosynthesis optimization 200. The optimize control parameterinputs/outputs might not be limited to those shown. For example,cost-benefit may be an additional input.

FIG. 3 illustrates a block diagram depicting the hardware components ofthe photosynthesis optimization system 100 of FIG. 1 , in accordancewith an exemplary embodiment of the present inventive concept.

It should be appreciated that FIG. 3 provides only an illustration ofone implementation and does not imply any limitations regarding theenvironments in which different embodiments may be implemented. Manymodifications to the depicted environment may be made.

Devices used herein may include one or more processors 302, one or morecomputer-readable RAMs 304, one or more computer-readable ROMs 306, oneor more computer readable storage media 308, device drivers 312,read/write drive or interface 314, network adapter or interface 316, allinterconnected over a communications fabric 318. Communications fabric318 may be implemented with any architecture designed for passing dataand/or control information between processors (such as microprocessors,communications and network processors, etc.), system memory, peripheraldevices, and any other hardware components within a system.

One or more operating systems 310, and one or more application programs311 are stored on one or more of the computer readable storage media 308for execution by one or more of the processors 302 via one or more ofthe respective RAMs 304 (which typically include cache memory). In theillustrated embodiment, each of the computer readable storage media 308may be a magnetic disk storage device of an internal hard drive, CD-ROM,DVD, memory stick, magnetic tape, magnetic disk, optical disk, asemiconductor storage device such as RAM, ROM, EPROM, flash memory orany other computer-readable tangible storage device that can store acomputer program and digital information.

Devices used herein may also include a R/W drive or interface 314 toread from and write to one or more portable computer readable storagemedia 326. Application programs 311 on said devices may be stored on oneor more of the portable computer readable storage media 326, read viathe respective R/W drive or interface 314 and loaded into the respectivecomputer readable storage media 308.

Devices used herein may also include a network adapter or interface 316,such as a TCP/IP adapter card or wireless communication adapter (such asa 4G wireless communication adapter using OFDMA technology). Applicationprograms 311 on said computing devices may be downloaded to thecomputing device from an external computer or external storage devicevia a network (for example, the Internet, a local area network or otherwide area network or wireless network) and network adapter or interface316. From the network adapter or interface 316, the programs may beloaded onto computer readable storage media 308. The network maycomprise copper wires, optical fibers, wireless transmission, routers,firewalls, switches, gateway computers and/or edge servers.

Devices used herein may also include a display screen 320, a keyboard orkeypad 322, and a computer mouse or touchpad 324. Device drivers 312interface to display screen 320 for imaging, to keyboard or keypad 322,to computer mouse or touchpad 324, and/or to display screen 320 forpressure sensing of alphanumeric character entry and user selections.The device drivers 312, R/W drive or interface 314 and network adapteror interface 316 may comprise hardware and software (stored on computerreadable storage media 308 and/or ROM 306).

The programs described herein are identified based upon the applicationfor which they are implemented in a specific one of the exemplaryembodiments. However, it should be appreciated that any particularprogram nomenclature herein is used merely for convenience, and thus theexemplary embodiments should not be limited to use solely in anyspecific application identified and/or implied by such nomenclature.

It is to be understood that although this disclosure includes a detaileddescription on cloud computing, implementation of the teachings recitedherein are not limited to a cloud computing environment. Rather, theexemplary embodiments of the present inventive concept are capable ofbeing implemented in conjunction with any other type of computingenvironment now known or later developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g., networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or data center).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported, providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer can deploy and runarbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure that includes anetwork of interconnected nodes.

FIG. 4 illustrates a cloud computing environment, in accordance with anexemplary embodiment of the present inventive concept.

As shown, cloud computing environment 50 may include one or more cloudcomputing nodes 40 with which local computing devices used by cloudconsumers, such as, for example, personal digital assistant (PDA) orcellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 40 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 4 are intended to be illustrative only and that computing nodes40 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

FIG. 5 illustrates abstraction model layers, in accordance with anexemplary embodiment of the present inventive concept.

Referring now to FIG. 5 , a set of functional abstraction layersprovided by cloud computing environment 50 (FIG. 4 ) is shown. It shouldbe understood in advance that the components, layers, and functionsshown in FIG. 5 are intended to be illustrative only and the exemplaryembodiments are not limited thereto. As depicted, the following layersand corresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may include applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfilment provide pre-arrangement for, and procurement of, cloudcomputing resources for which a future requirement is anticipated inaccordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and photosynthesis optimization 96.

The exemplary embodiments of the present inventive concept may be asystem, a method, and/or a computer program product at any possibletechnical detail level of integration. The computer program product mayinclude a computer readable storage medium (or media) having computerreadable program instructions thereon for causing a processor to carryout aspects of the present inventive concept.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present inventive concept may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present inventive concept.

Aspects of the present inventive concept are described herein withreference to flowchart illustrations and/or block diagrams of methods,apparatus (systems), and computer program products according toexemplary embodiments. It will be understood that each block of theflowchart illustrations and/or block diagrams, and combinations ofblocks in the flowchart illustrations and/or block diagrams, can beimplemented by computer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a computer, or other programmable data processing apparatusto produce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks. These computerreadable program instructions may also be stored in a computer readablestorage medium that can direct a computer, a programmable dataprocessing apparatus, and/or other devices to function in a particularmanner, such that the computer readable storage medium havinginstructions stored therein comprises an article of manufactureincluding instructions which implement aspects of the function/actspecified in the flowchart and/or block diagram block or blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present inventive concept. In this regard, each blockin the flowchart or block diagrams may represent a module, segment, orportion of instructions, which comprises one or more executableinstructions for implementing the specified logical function(s). In somealternative implementations, the functions noted in the blocks may occurout of the order noted in the Figures. For example, two blocks shown insuccession may, in fact, be accomplished as one step, executedconcurrently, substantially concurrently, in a partially or whollytemporally overlapping manner, or the blocks may sometimes be executedin the reverse order, depending upon the functionality involved. It willalso be noted that each block of the block diagrams and/or flowchartillustration, and combinations of blocks in the block diagrams and/orflowchart illustration, can be implemented by special purposehardware-based systems that perform the specified functions or acts orcarry out combinations of special purpose hardware and computerinstructions.

Based on the foregoing, a computer system, method, and computer programproduct have been disclosed. However, numerous modifications, additions,and substitutions can be made without deviating from the scope of theexemplary embodiments of the present inventive concept. Therefore, theexemplary embodiments of the present inventive concept have beendisclosed by way of example and not by limitation.

1. A method for photosynthesis optimization, the method comprising:determining ambient levels of at least one gas, water, and sunlight at alocation; selecting a catalyst to perform an artificial photosynthesisreaction at the location; determining at least one limiting factor forthe artificial photosynthesis reaction based on the catalyst and theambient levels; and compensating for the at least one limiting factor.2. The method of claim 1, wherein the determining ambient levels of theat least one gas, water, and sunlight at the location is performed bymachine learning based on at least one of a weather forecast, previouslyrecorded ambient levels of the at least one gas, water, and sunlight,and satellite imaging.
 3. The method of claim 1, wherein at least one ofthe location and the catalyst is chosen based on the ambient levels ofthe at least one gas, water, and sunlight.
 4. The method of claim 1,wherein the at least one gas includes carbon dioxide, and wherein theambient levels of carbon dioxide are determined using at least one ofsatellite imaging and carbon sequestration at the location.
 5. Themethod of claim 1, further comprising: performing a cost-benefitanalysis of compensating for the at least one limiting factor; andimplementing a compensatory option from a plurality of compensatoryoptions based on the cost-benefit-analysis.
 6. The method of claim 5,wherein the compensatory option is performed by adjusting at least oneof an IoT controlled solar irradiance mirror, an artificial lightsource, a gas inlet, and a humidity controller.
 7. The method of claim1, wherein the catalyst is a photocatalyst, bio-electrochemicalcatalyst, or a photochemical catalyst, and wherein the selecting thecatalyst is based on a humidity, irradiance, and carbon dioxidesensitivity profile and knowledge base.
 8. The method of claim 7,further comprising: generating a three-dimensional lookup table for thecatalyst based on the humidity, irradiance, and carbon dioxidesensitivity profile.
 9. A computer program product for photosynthesisoptimization, the computer program product comprising: one or morenon-transitory computer-readable storage media and program instructionsstored on the one or more non-transitory computer-readable storage mediacapable of performing a method, the method comprising: determiningambient levels of at least one gas, water, and sunlight at a location;selecting a catalyst to perform an artificial photosynthesis reaction atthe location; determining at least one limiting factor for theartificial photosynthesis reaction based on the catalyst and the ambientlevels; and compensating for the at least one limiting factor.
 10. Themethod of claim 9, wherein the determining ambient levels of the atleast one gas, water, and sunlight at the location is performed bymachine learning based on at least one of a weather forecast, previouslyrecorded ambient levels of the at least one gas, water, and sunlight,and satellite imaging.
 11. The method of claim 9, wherein at least oneof the location and the catalyst is chosen based on the ambient levelsof the at least one gas, water, and sunlight.
 12. The method of claim 9,wherein the at least one gas includes carbon dioxide, and wherein theambient levels of carbon dioxide are determined using at least one ofsatellite imaging and carbon sequestration at the location.
 13. Themethod of claim 9, further comprising: performing a cost-benefitanalysis of compensating for the at least one limiting factor; andimplementing a compensatory option from a plurality of compensatoryoptions based on the cost-benefit-analysis.
 14. The method of claim 13,wherein the compensatory option is performed by adjusting at least oneof an IoT controlled solar irradiance mirror, an artificial lightsource, a gas inlet, and a humidity controller.
 15. A computer systemfor photosynthesis optimization, the system comprising: one or morecomputer processors, one or more computer-readable storage media, andprogram instructions stored on the one or more of the computer-readablestorage media for execution by at least one of the one or moreprocessors capable of performing a method, the method comprising:determining ambient levels of at least one gas, water, and sunlight at alocation; selecting a catalyst to perform an artificial photosynthesisreaction at the location; determining at least one limiting factor forthe artificial photosynthesis reaction based on the catalyst and theambient levels; and compensating for the at least one limiting factor.16. The method of claim 15, wherein the determining ambient levels ofthe at least one gas, water, and sunlight at the location is performedby machine learning based on at least one of a weather forecast,previously recorded ambient levels of the at least one gas, water, andsunlight, and satellite imaging.
 17. The method of claim 15, wherein atleast one of the location and the catalyst is chosen based on theambient levels of the at least one gas, water, and sunlight.
 18. Themethod of claim 15, wherein the at least one gas includes carbondioxide, and wherein the ambient levels of carbon dioxide are determinedusing at least one of satellite imaging and carbon sequestration at thelocation.
 19. The method of claim 15, further comprising: performing acost-benefit analysis of compensating for the at least one limitingfactor; and implementing a compensatory option from a plurality ofcompensatory options based on the cost-benefit-analysis.
 20. The methodof claim 19, wherein the compensatory option is performed by adjustingat least one of an IoT controlled solar irradiance mirror, an artificiallight source, a gas inlet, and a humidity controller.